Address:

Location: Toronto, ON (Hybrid)

Overview

BMO is seeking a Senior Machine Learning Engineer to join our team in Toronto. This role is ideal for an experienced technology professional with expertise in Machine Learning, Python development, AWS cloud technologies, and solution design who is passionate about leading the delivery of innovative AI and machine learning solutions at enterprise scale.

As a Senior Machine Learning Engineer, you will lead complex machine learning and AI initiatives from concept through implementation. Working closely with business stakeholders, product teams, architects, data engineers, and technology partners, you will translate business objectives into scalable technical solutions and drive projects through all stages of the delivery lifecycle.

The successful candidate will combine strong technical expertise with solution design and project leadership capabilities. You will be responsible for leading the delivery of strategic initiatives, influencing technical direction, and ensuring the successful implementation of secure, scalable, and high-performing machine learning solutions that deliver measurable business value.

Key Responsibilities

• Lead the delivery of machine learning and AI initiatives from requirements definition through design, development, deployment, and production support.

• Partner with business and technology stakeholders to understand objectives, define technical approaches, and develop implementation roadmaps.

• Drive end-to-end execution of complex projects, coordinating activities across engineering, data, infrastructure, security, and platform teams.

• Design scalable, resilient, and maintainable machine learning solutions aligned with enterprise architecture standards and business objectives.

• Develop and maintain production-ready applications and machine learning services using Python and modern software engineering practices.

• Lead solution design activities and contribute to architectural decisions that support long-term scalability, reliability, and operational excellence.

• Design and implement cloud-native applications and services leveraging AWS technologies.

• Build, deploy, and optimize machine learning models in production environments.

• Drive the adoption of MLOps practices, including CI/CD, model lifecycle management, monitoring, automation, and observability.

• Ensure solutions meet security, compliance, performance, and operational requirements.

• Identify project risks, dependencies, and technical challenges, developing mitigation strategies to support successful delivery.

• Collaborate with Enterprise Architecture and engineering teams to ensure alignment with broader technology strategies and standards.

• Evaluate emerging technologies and recommend improvements that enhance platform capabilities, scalability, and business outcomes.

• Lead troubleshooting and root cause analysis activities for complex production issues.

• Support project planning, estimation, and technical delivery activities across multiple concurrent initiatives.

• Apply BMO's Risk Management Framework and adhere to all applicable regulatory, security, and governance standards.

Required Technical Skills

Python & Software Engineering

• Advanced proficiency in Python application development.

• Strong experience building enterprise-grade applications, APIs, and machine learning services.

• Hands-on experience with libraries and frameworks such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, FastAPI, or equivalent technologies.

• Strong understanding of software design patterns, testing methodologies, code quality standards, and modern development practices.

AWS Cloud & Solution Design

• Strong experience designing and implementing cloud-native solutions in AWS.

• Experience with AWS services including SageMaker, Lambda, API Gateway, S3, IAM, CloudWatch, EventBridge, ECS, EKS, and related technologies.

• Strong knowledge of serverless, distributed, and event-driven architectures.

• Proven ability to design scalable, secure, and highly available solutions supporting enterprise workloads.

Machine Learning & MLOps

• Experience developing, deploying, monitoring, and optimizing machine learning models in production environments.

• Strong understanding of machine learning techniques, feature engineering, model evaluation, and deployment best practices.

• Experience implementing MLOps capabilities, CI/CD pipelines, automation frameworks, and model governance practices.

• Knowledge of model monitoring, observability, and operational excellence principles.

Qualifications

Required

• 7+ years of experience in Software Engineering, Machine Learning Engineering, Artificial Intelligence, or a related technology discipline.

• Proven experience leading the delivery of complex machine learning, AI, data, or cloud technology initiatives within enterprise environments.

• Demonstrated experience translating business requirements into technical solutions, architecture designs, and implementation plans.

• Strong experience working across cross-functional teams, including business, data, engineering, and infrastructure stakeholders.

• Advanced proficiency in Python development.

• Strong experience designing and implementing cloud-native solutions using AWS.

• Experience with APIs, microservices, distributed systems, and modern architecture patterns.

• Strong understanding of software engineering principles, DevOps practices, testing methodologies, and the software development lifecycle.

• Experience with Git, CI/CD pipelines, and Agile delivery methodologies.

• Excellent analytical, problem-solving, communication, and stakeholder management skills.

Preferred

• Experience delivering enterprise-scale AI and Machine Learning platforms and solutions.

• Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI-powered applications.

• Experience with AWS SageMaker and cloud-based machine learning platforms.

• Experience with containerization technologies, Kubernetes, and platform engineering practices.

• AWS certifications such as Solutions Architect or Machine Learning Specialty.

• Experience within financial services or other highly regulated industries.

• Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, or a related field.

The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: